Degree | Type | Year | Semester |
---|---|---|---|
2503852 Applied Statistics | OB | 3 | 2 |
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
It is assumed that the student taking this subject has acquired the skills of the subjects of
You will need a good level and practice in programming with R.
Learn what Bayesian Networks (BN) are and how they are used: BN are a probabilistic model used in Supervised Machine Learning that describe the probabilistic relationships between variables that affect a given phenomenon of interest (which can be a complex system) and can be used as classifiers.
Understand how Bayesian Networks are used to assess and quantify risks, among other applications.
Know different methodologies that will have to be applied, or not, when working with these models, in the pre-process phase of the database depending on its characteristics or in the construction phase of the predictive model.
Know different behavioral metrics to validatethemodel and understand its usefulness and adequacy, depending on the characteristics of the database.
Learn how to build R scripts that allow you to learn these models from a database and do their validation, using the relevant libraries. Apply it with real data.
The subject is structured around theoretical classes, problems and practices. The follow-up of the subject is face-to-face, but it will be necessary to extend the teacher’s explanations with the student’s autonomous study, with the support of the reference bibliography and the material provided by the teacher.
The problem class will focus on solving some of the proposed problems. In the practical classes we will work with R and his libraries. Student participation in problem and practice classes will be especially valued.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Practices (deliveries, controls) | 12 | 0.48 | 1, 3, 9, 8 |
Problems | 14 | 0.56 | 2, 4, 14, 5, 6, 13, 15 |
Theory | 26 | 1.04 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
Type: Supervised | |||
Tutorials | 10 | 0.4 | 10, 3, 11, 12, 7 |
Type: Autonomous | |||
Practical work with computer tools | 30 | 1.2 | 1, 3, 9, 8, 15 |
Study and think problems | 40 | 1.6 | 4, 14, 5, 6, 13, 15 |
The final grade for this subject is obtained as the weighted average of the grades of:
The PAC1 and PAC2 continuous assessment tests consist of a delivery of problems/practical exercises/work with R, which will be specified throughout the course, and in their development in face-to-face classes throughout the semester.
Only those notes that are at least 3.5 out of 10 will be taken into account in the calculation of the weighted average (those that do not comply will weight 0).
To pass the subject it is necessary that this average is at least 5.0 out of 10. In case of not passing the subject in the first call, the student can present himself for recovery. The retake exam represents 100% of the final grade for those students who take the retake, which can only be students who have not passed the subject on the first call (the retake exam does not serve to improve the grade for students who have already passed).
The student who has presented the PAC1 or PAC2 deliveries, or has presented the exam or the recovery exam will be considered evaluable. Otherwise, it will be recorded in the minutes as Not Assessable.
For the eventual assignment of Honors, the marks of the second call will not be taken into account.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exam | 60% | 3 | 0.12 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
PAC1 | 20% | 6 | 0.24 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
PAC2 | 20% | 9 | 0.36 | 2, 1, 10, 3, 9, 4, 14, 5, 6, 8, 13, 11, 12, 7, 15 |
The R software will be used with some libraries that will be indicated in due course throughout the course. Preferably in the RStudio environment.